Using Surrogate Endpoints for Decision-Making in Adaptive Seamless Designs

临床试验用于在一般使用之前评估新药和其他新疗法的安全性和有效性。通常，该评价的主要部分是比较一种新药（单一预先指定的剂量/制剂）与现有标准或对照治疗的试验。近年来，临床试验设计的一项创新是在一项临床试验中同时比较几种新的治疗方法与对照组，根据早期结果将疗效较差的治疗方法从试验中剔除。这种方法被称为自适应无缝设计，有可能减少所需的患者数量。在慢性病中，当只有在长期随访之后才能观察到表明治疗效果的数据时，会出现一个特殊的挑战。例如，在多发性硬化症中，考虑的主要结局可能是治疗三年后健康状况的变化。通常可以观察到其他一些早期结果，认为这些结果可以预测主要的长期结果。这样的结果称为替代终点。例如，在多发性硬化症的情况下，大脑成像可以给出疾病进展的早期指示。在这种情况下，除了任何可用的主要终点数据外，使用该替代终点信息来决定哪些治疗应从试验中删除似乎是明智的。在过去几年中，允许在自适应无缝设计中使用替代终点进行治疗选择的统计方法的开发一直是一个活跃的研究领域。当试图确保所选治疗（或治疗）的最终比较是公平的时，困难就来了，因为它们可能是因为偶然出现上级于其他治疗而被选择的。本项目的目的是开发一种新的统计方法，用于在自适应无缝设计中使用替代终点。通过最有效地利用替代终点信息，这种新方法将进一步减少试验所需的患者数量。这将减少接受效果较差的治疗的患者人数，并确保在彻底评估的基础上尽快提供有效的新药供普遍使用。

Clinical trials are used to evaluate the safety and effectiveness of new drugs and other novel therapies prior to general use. Conventionally, a major part of this evaluation is a trial to compare one new drug (at a single prespecified dose/formulation) with an existing standard or control treatment. Statistical methods have been developed over many years to allow valid interpretation of the data from such trials.A recent innovation in clinical trial design has been the development of new approaches in which several new treatments are compared simultaneously with the control in a single clinical trial, with the less effective treatments dropped from the trial on the basis of early results. Such methods, called adaptive seamless designs, have the potential to reduce the number of patients required. A particular challenge arises in chronic diseases when the data indicating treatment effectiveness may be observed only after long-term follow-up. For example, in Multiple Sclerosis the primary outcome considered might be a change in health status after three years of treatment. Often some other earlier outcome can be observed that it is believed is predictive of the primary long-term outcome. Such an outcome is called a surrogate endpoint. In the Multiple Sclerosis case, for example, imaging of the brain can give an early indication of disease progression. In this case it would seem sensible to use this surrogate endpoint information in addition to any available primary endpoint data to decide which treatments should be dropped from the trial. The development of statical methods that allow the use of surrogate endpoints for treatment selection in adaptive seamless designs has been an area of active research in the last few years. The difficulty comes when trying to ensure that the final comparison of the selected treatment (or treatments) is a fair one, since they could have been selected because they appeared superior to other treatments by chance.The aim of this project is to develop a new statistical method for using a surrogate endpoint in an adaptive seamless design. By making the most efficient use of the surrogate endpoint information, this new approach will lead to further reduction in the number of patients required in the trial. This will reduce the number of patients exposed to less effective treatments and ensure that effective new drugs are made available for general use, on the basis of thorough evaluation, as quickly as possible.